Exploring CAGE data in R

• Data formats for downstream manipulation
• Annotation for genomic features
• Promoter classification
• Oligonucleotide heatmaps

dataframes & matrices returned by CAGEr functions:

• Per individual sample ( raw or normalized tag counts)

  • CTSS
  • Tag clusters & interquantile widths
  • Consensus clusters & interquantile widths
    
    

• Across samples (one output for multiple samples)

  • Consensus clusters & interquantile widths
    

These can be easily manipulated by the user in R.

We'll go through this in more detail in the tutorial

Other than consensus clusters most data is extracted per sample as they are sample specific

• genomic coordinates of CTSS
• genomic coordinates of Tag clusters

Likely you want to perform the same analyses, checks, plots, etc for all the samples.

Solution?

Automate what you're doing! Functions are great to avoid mistakes

• create a list for example that contain all the info of samples per slot

We'll create a few of these in the tutorial

Where does most of the signal fall?

Nepal, C., et al. (2013). Dynamic regulation of coding and non-coding transcription initiation landscape at single nucleotide resolution during vertebrate embryogenesis. Genome Research, 23(11):1938-1950.

we count the overlap our TCs per sample for each feature and count the occurances per sample:

• promoter
• 5kb upstream of promoter
• exons
• introns
• gene

Distinguishing features of sharp and broad promoters

• Genomic position of dominant TSS
• Add a window (200 bp for example) around it
• Plot the average oligonucleotide profiles

Based on interquantile widths: sharp < 10 & broad >= 10

Taken from R package: seqPattern.

The order of sequences is important when looking for architectures:

Similar sample but ordered differently

The tutorial that is linked with this presentation:

Tutorials dir

CAGE-workshop-Tutorial-P3-ExploringCageInR